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Deep-learning for 3D reconstruction / Fabio Tosi (2021)  

Public Titre : Deep-learning for 3D reconstruction Type de document : Thèse/HDR Auteurs : Fabio Tosi, Auteur Editeur : Bologne [Italie] : Université de Bologne Année de publication : 2021 Format : 21 x 30 cm Note générale : bibliographie PhD Thesis in Computer Science and Engineering Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] carte de confiance [Termes IGN] compréhension de l'image [Termes IGN] profondeur [Termes IGN] reconstruction 3D [Termes IGN] réseau antagoniste génératif [Termes IGN] vision stéréoscopique Résumé : (auteur) Depth perception is paramount for many computer vision applications such as autonomous driving and augmented reality. Despite active sensors (e.g., LiDAR, Time-of-Flight, struc- tured light) are quite diffused, they have severe shortcomings that could be potentially addressed by image-based sensors. Concerning this latter category, deep learning has enabled ground-breaking results in tackling well-known issues affecting the accuracy of systems inferring depth from a single or multiple images in specific circumstances (e.g., low textured regions, depth discontinuities, etc.), but also introduced additional concerns about the domain shift occurring between training and target environments and the need of proper ground truth depth labels to be used as the training signals in network learning. Moreover, despite the copious literature concerning confidence estimation for depth from a stereo setup, inferring depth uncertainty when dealing with deep networks is still a major challenge and almost unexplored research area, especially when dealing with a monocular setup. Finally, computational complexity is another crucial aspect to be considered when targeting most practical applications and hence is desirable not only to infer reliable depth data but do so in real-time and with low power requirements even on standard embedded devices or smartphones. Therefore, focusing on stereo and monocular setups, this thesis tackles major issues affecting methodologies to infer depth from images and aims at developing accurate and efficient frameworks for accurate 3D reconstruction on challenging environments. Note de contenu : Introduction 1- Related work 2- Datasets 3- Evaluation protocols 4- Confidence measures in a machine learning world 5- Efficient confidence measures for embedded stereo 6- Even more confident predictions with deep machine-learning 7- Beyond local reasoning for stereo confidence estimation with deep learning 8- Good cues to learn from scratch a confidence measure for passive depth sensors 9- Confidence estimation for ToF and stereo sensors and its application to depth data fusion 10- Learning confidence measures in the wild 11- Self-adapting confidence estimation for stereo 12- Leveraging confident points for accurate depth refinement on embedded systems 13- SMD-Nets: Stereo Mixture Density Networks 14- Real-time self-adaptive deep stereo 15- Guided stereo matching 16- Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation 17- Learning end-to-end scene flow by distilling single tasks knowledge 18- Learning monocular depth estimation with unsupervised trinocular assumptions 19- Geometry meets semantics for semi-supervised monocular depth estimation 20- Generative Adversarial Networks for unsupervised monocular depth prediction 21- Learning monocular depth estimation infusing traditional stereo knowled 22- Towards real-time unsupervised monocular depth estimation on CPU 23- Enabling energy-efficient unsupervised monocular depth estimation on ARMv7-based platforms 24- Distilled semantics for comprehensive scene understanding from videos 25- On the uncertainty of self-supervised monocular depth estimation Conclusion Numéro de notice : 28596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Computer Science and Engineering : Bologne : 2021 DOI : 10.48676/unibo/amsdottorato/9816 En ligne : http://amsdottorato.unibo.it/9816/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99325